Point set augmentation through fitting for enhanced ICP registration of point clouds in multisensor coordinate metrology

2013 ◽  
Vol 29 (1) ◽  
pp. 39-52 ◽  
Author(s):  
N. Senin ◽  
B.M. Colosimo ◽  
M. Pacella
Keyword(s):  
Point Clouds ◽  
Coordinate Metrology ◽  
Point Set

Implementing Point Assignment Using Octrees and GPU

2007 ◽  
Author(s):  
Koushik V. Aravalli ◽  
Thomas R. Kurfess ◽  
Thomas M. Tucker
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Computer Hardware ◽  
Model Complexity ◽  
Data Sets ◽  
Cad Model ◽  
Point Set Registration ◽  
Coordinate Metrology ◽  
Point Set ◽  
Processor Unit

Data point set registration is an important operation in coordinate metrology. Registration is the operation by which sampled point clouds are aligned with a CAD model by a 4×4 homogeneous transformation (e.g., rotation and translation). This alignment permits validation of the produced artifact’s geometry. Registration is an iterative nonlinear optimization operation assigning points on the CAD model for the sampled points. The objective is to minimize the sum of the squares of the normal distances between each point in the point cloud and the closest point in the CAD model. State-of-the-art metrology systems are now capable of generating thousands, if not millions, of data points during an inspection operation, resulting in increased computational power to fully utilize these larger data sets. The execution time for assigning the point set in registration process is directly related to the number of points processed and CAD model complexity. A brute force approach to registration, which is often used, is to compute the minimum distance between each sampled point and its normal projection on the CAD model. As the point cloud size and CAD model complexity increase, this approach becomes intractable and inefficient. This paper proposes a new approach to efficiently identify the closest point in the CAD model for a given point. This approach employs a combination of readily available computer hardware, graphical processor unit (GPU) and a formulation of the point assignment problem, using an octree data structure that is suited for execution on the GPU.

Three Dimensional Imaging of LIGA-Made Microcomponents

2004 ◽  
Vol 126 (4) ◽  
pp. 813-821 ◽  
Author(s):  
Douglas Chinn ◽  
Peter Ostendorp ◽  
Mike Haugh ◽  
Russell Kershmann ◽  
Thomas Kurfess ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
High Speed ◽  
Three Dimensional ◽  
Point Clouds ◽  
Three Dimensions ◽  
Cad Model ◽  
Volumetric Imaging ◽  
Data Registration ◽  
Liga Technology ◽  
Coordinate Metrology

Nickel and nickel-alloy microparts sized on the order of 5–1000 microns have been imaged in three dimensions using a new microscopic technique, Digital Volumetric Imaging (DVI). The gears were fabricated using Sandia National Laboratories’ LIGA technology (lithography, molding, and electroplating). The images were taken on a microscope built by Resolution Sciences Corporation by slicing the gear into one-micron thin slices, photographing each slice, and then reconstructing the image with software. The images were matched to the original CAD (computer aided design) model, allowing LIGA designers, for the first time, to see visually how much deviation from the design is induced by the manufacturing process. Calibration was done by imaging brass ball bearings and matching them to the CAD model of a sphere. A major advantage of DVI over scanning techniques is that internal defects can be imaged to very high resolution. In order to perform the metrology operations on the microcomponents, high-speed and high-precision algorithms are developed for coordinate metrology. The algorithms are based on a least-squares approach to data registration the {X,Y,Z} point clouds generated from the component surface onto a target geometry defined in a CAD model. Both primitive geometric element analyses as well as an overall comparison of the part geometry are discussed. Initial results of the micromeasurements are presented in the paper.

Construction of Point Set Surfaces through Quadric Polynomials

2014 ◽  
Vol 565 ◽  
pp. 253-259
Author(s):  
Yu Liu
Keyword(s):  
Vector Field ◽  
Energy Function ◽  
Field Experiments ◽  
Point Clouds ◽  
Weighted Sum ◽  
Input Point ◽  
Point Set ◽  
Point Set Surfaces ◽  
Normal Vectors ◽  
Sum Of Distances

This paper constructs PSSs (Point Set Surfaces) by combining locally fitted quadric polynomials. First, an energy function is defined as the weighted sum of distances from a point to these quadric polynomials. Then, a vector field is constructed by the weighted sum of normal vectors at input points. Finally, points on a PSS are obtained by finding local minima of the energy function along the vector field. Experiments demonstrate that high quality PSSs can be obtained from the method presented for input point clouds sampled from various shapes.

scholarly journals Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution

2020 ◽  
Vol 34 (07) ◽  
pp. 12717-12724
Author(s):  
Yang You ◽  
Yujing Lou ◽  
Qi Liu ◽  
Yu-Wing Tai ◽  
Lizhuang Ma ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Point Cloud ◽  
Data Augmentation ◽  
Feature Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
Rotation Invariant ◽  
Learning Framework ◽  
Point Set ◽  
Part Segmentation

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point. Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. We show that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide theoretical analysis for the rotation-invariance achieved by our methods.

scholarly journals Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

2019 ◽  
Vol 11 (23) ◽  
pp. 2846 ◽  
Author(s):  
Tong ◽  
Li ◽  
Zhang ◽  
Chen ◽  
Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Single Point ◽  
Point Clouds ◽  
Max Pooling ◽  
Multi Scale ◽  
Cloud Classification ◽  
Point Set ◽  
Multi Level ◽  
Point Cloud Classification

Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.

scholarly journals AUTOMATIC MONITORING OF TUNNEL DEFORMATION BASED ON HIGH DENSITY POINT CLOUDS DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 353-360 ◽  
Author(s):  
L. Du ◽  
R. Zhong ◽  
H. Sun ◽  
Q. Wu
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
Point Clouds ◽  
Central Axis ◽  
Two Dimensional ◽  
Density Point ◽  
Point Set ◽  
The Cross ◽  
Projection Point ◽  
Dimensional Surface

An automated method for tunnel deformation monitoring using high density point clouds data is presented. Firstly, the 3D point clouds data are converted to two-dimensional surface by projection on the XOY plane, the projection point set of central axis on XOY plane named U<sub>xoy</sub> is calculated by combining the Alpha Shape algorithm with RANSAC (Random Sampling Consistency) algorithm, and then the projection point set of central axis on YOZ plane named Uyoz is obtained by highest and lowest points which are extracted by intersecting straight lines that through each point of U<sub>xoy</sub> and perpendicular to the two -dimensional surface with the tunnel point clouds, U<sub>xoy</sub> and U<sub>yoz</sub> together form the 3D center axis finally. Secondly, the buffer of each cross section is calculated by K-Nearest neighbor algorithm, and the initial cross-sectional point set is quickly constructed by projection method. Finally, the cross sections are denoised and the section lines are fitted using the method of iterative ellipse fitting. In order to improve the accuracy of the cross section, a fine adjustment method is proposed to rotate the initial sectional plane around the intercept point in the horizontal and vertical direction within the buffer. The proposed method is used in Shanghai subway tunnel, and the deformation of each section in the direction of 0 to 360 degrees is calculated. The result shows that the cross sections becomes flat circles from regular circles due to the great pressure at the top of the tunnel

scholarly journals AIRBORNE LIDAR POINT CLOUD CLASSIFICATION BASED ON MULTILEVEL POINT CLUSTER FEATURES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1231-1237
Author(s):  
Y. Gao ◽  
M. C. Li
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Location Based Services ◽  
Training Data ◽  
Cloud Classification ◽  
Point Set ◽  
Point Cloud Classification

Abstract. Airborne Light Detection And Ranging (LiDAR) has become an important means for efficient and high-precision acquisition of 3D spatial data of large scenes. It has important application value in digital cities and location-based services. The classification and identification of point cloud is the basis of its application, and it is also a hot and difficult problem in the field of geographic information science.The difficulty of LiDAR point cloud classification in large-scale urban scenes is: On the one hand, the urban scene LiDAR point cloud contains rich and complex features, many types of features, different shapes, complex structures, and mutual occlusion, resulting in large data loss; On the other hand, the LiDAR scanner is far away from the urban features, and is like a car, a pedestrian, etc., which is in motion during the scanning process, which causes a certain degree of data noise of the point cloud and uneven density of the point cloud.Aiming at the characteristics of LiDAR point cloud in urban scene.The main work of this paper implements a method based on the saliency dictionary and Latent Dirichlet Allocation (LDA) model for LiDAR point cloud classification. The method uses the tag information of the training data and the tag source of each dictionary item to construct a significant dictionary learning model in sparse coding to expresses the feature of the point set more accurately.And it also uses the multi-path AdaBoost classifier to perform the features of the multi-level point set. The classification of point clouds is realized based on the supervised method. The experimental results show that the feature set extracted by the method combined with the multi-path classifier can significantly improve the cloud classification accuracy of complex city market attractions.

scholarly journals Geometric Multi-Model Fitting by Deep Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 10081-10082
Author(s):  
Zongliang Zhang ◽  
Hongbin Zeng ◽  
Jonathan Li ◽  
Yiping Chen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Model Fitting ◽  
Simulated Data ◽  
Point Clouds ◽  
Sequential Decision ◽  
Optimal Decisions ◽  
Best Fitting ◽  
Point Set

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.

Seed point set-based building roof extraction from airborne LiDAR point clouds using a top-down strategy

2021 ◽  
Vol 126 ◽  
pp. 103660
Author(s):  
Jie Shao ◽  
Wuming Zhang ◽  
Aojie Shen ◽  
Nicolas Mellado ◽  
Shangshu Cai ◽  
...  
Keyword(s):  
Point Clouds ◽  
Airborne Lidar ◽  
Top Down ◽  
Seed Point ◽  
Building Roof ◽  
Point Set
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